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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
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Rapid mixing for the hardcore Glauber dynamics and other Markov chains in bounded-treewidth graphs

Authors: Eppstein, D; Frishberg, D;

Rapid mixing for the hardcore Glauber dynamics and other Markov chains in bounded-treewidth graphs

Abstract

We give a new rapid mixing result for a natural random walk on the independent sets of a graph $G$. We show that when $G$ has bounded treewidth, this random walk -- known as the Glauber dynamics for the hardcore model -- mixes rapidly for all fixed values of the standard parameter $λ> 0$, giving a simple alternative to existing sampling algorithms for these structures. We also show rapid mixing for analogous Markov chains on dominating sets, $b$-edge covers, $b$-matchings, maximal independent sets, and maximal $b$-matchings. (For $b$-matchings, maximal independent sets, and maximal $b$-matchings we also require bounded degree.) Our results imply simpler alternatives to known algorithms for the sampling and approximate counting problems in these graphs. We prove our results by applying a divide-and-conquer framework we developed in a previous paper, as an alternative to the projection-restriction technique introduced by Jerrum, Son, Tetali, and Vigoda. We extend this prior framework to handle chains for which the application of that framework is not straightforward, strengthening existing results by Dyer, Goldberg, and Jerrum and by Heinrich for the Glauber dynamics on $q$-colorings of graphs of bounded treewidth and bounded degree.

43 pages, 4 figures

Keywords

FOS: Computer and information sciences, Discrete Mathematics (cs.DM), Glauber dynamics, multicommodity flow, 004, mixing time, Computer Science - Data Structures and Algorithms, Information and computing sciences, Data Structures and Algorithms (cs.DS), projection-restriction, Computer Science - Discrete Mathematics, ddc: ddc:004

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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